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    <abstract>In this paper, we examine how large language models (LLMs) solve multi-step problems under a language agent framework with three components: a generator, a discriminator, and a planning method. We investigate the practical utility of two advanced planning methods, iterative correction and tree search. We present a comprehensive analysis of how discrimination accuracy affects the overall performance of agents when using these two methods or a simpler method, re-ranking. Experiments on two tasks, text-to-SQL parsing and mathematical reasoning, show that: (1) advanced planning methods demand discriminators with at least 90% accuracy to achieve significant improvements over re-ranking; (2) current LLMs’ discrimination abilities have not met the needs of advanced planning methods to achieve such improvements; (3) with LLM-based discriminators, advanced planning methods may not adequately balance accuracy and efficiency. For example, compared to the other two methods, tree search is at least 10–20 times slower but leads to negligible performance gains, which hinders its real-world applications.</abstract>
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%0 Conference Proceedings
%T When is Tree Search Useful for LLM Planning? It Depends on the Discriminator
%A Chen, Ziru
%A White, Michael
%A Mooney, Ray
%A Payani, Ali
%A Su, Yu
%A Sun, Huan
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F chen-etal-2024-tree
%X In this paper, we examine how large language models (LLMs) solve multi-step problems under a language agent framework with three components: a generator, a discriminator, and a planning method. We investigate the practical utility of two advanced planning methods, iterative correction and tree search. We present a comprehensive analysis of how discrimination accuracy affects the overall performance of agents when using these two methods or a simpler method, re-ranking. Experiments on two tasks, text-to-SQL parsing and mathematical reasoning, show that: (1) advanced planning methods demand discriminators with at least 90% accuracy to achieve significant improvements over re-ranking; (2) current LLMs’ discrimination abilities have not met the needs of advanced planning methods to achieve such improvements; (3) with LLM-based discriminators, advanced planning methods may not adequately balance accuracy and efficiency. For example, compared to the other two methods, tree search is at least 10–20 times slower but leads to negligible performance gains, which hinders its real-world applications.
%R 10.18653/v1/2024.acl-long.738
%U https://aclanthology.org/2024.acl-long.738
%U https://doi.org/10.18653/v1/2024.acl-long.738
%P 13659-13678

Markdown (Informal)

[When is Tree Search Useful for LLM Planning? It Depends on the Discriminator](https://aclanthology.org/2024.acl-long.738) (Chen et al., ACL 2024)

• When is Tree Search Useful for LLM Planning? It Depends on the Discriminator (Chen et al., ACL 2024)

ACL

• Ziru Chen, Michael White, Ray Mooney, Ali Payani, Yu Su, and Huan Sun. 2024. When is Tree Search Useful for LLM Planning? It Depends on the Discriminator. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 13659–13678, Bangkok, Thailand. Association for Computational Linguistics.